COVID_19 Dataset

Import Required Libraries

import pandas as pd
import plotly.express as px
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
!pip install pandasql
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())

Requirement already satisfied: pandasql in c:\programdata\anaconda3\lib\site-packages (0.7.3)
Requirement already satisfied: sqlalchemy in c:\programdata\anaconda3\lib\site-packages (from pandasql) (1.3.20)
Requirement already satisfied: pandas in c:\programdata\anaconda3\lib\site-packages (from pandasql) (1.1.3)
Requirement already satisfied: numpy in c:\programdata\anaconda3\lib\site-packages (from pandasql) (1.19.2)
Requirement already satisfied: python-dateutil>=2.7.3 in c:\programdata\anaconda3\lib\site-packages (from pandas->pandasql) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in c:\programdata\anaconda3\lib\site-packages (from pandas->pandasql) (2020.1)
Requirement already satisfied: six>=1.5 in c:\programdata\anaconda3\lib\site-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)

Data Exploration

Read csv file

CVD=pd.read_csv("PK COVID-19-3jun.csv")

Display whole dataset

CVD

	Date	Cases	Deaths	Recovered	Travel_history	Province	City
0	2/26/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
1	2/26/2020	2	0	0	Iran/Taftan	Sindh	Karachi
2	2/29/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
3	2/29/2020	1	0	0	Iran/Taftan	Sindh	Karachi
4	3/2/2020	1	0	0	Iran/Taftan	Gilgit-Baltistan	Gilgit
...	...	...	...	...	...	...	...
2793	5/30/2020	952	36	394	NaN	Punjab	Lahore
2794	5/31/2020	1184	22	120	NaN	Punjab	Lahore
2795	6/1/2020	1610	43	95	NaN	Punjab	Lahore
2796	6/2/2020	1639	30	353	NaN	Punjab	Lahore
2797	6/3/2020	1615	37	243	NaN	Punjab	Lahore

2798 rows × 7 columns

Display First Five Records of dataset

CVD.head()

	Date	Cases	Deaths	Recovered	Travel_history	Province	City
0	2/26/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
1	2/26/2020	2	0	0	Iran/Taftan	Sindh	Karachi
2	2/29/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
3	2/29/2020	1	0	0	Iran/Taftan	Sindh	Karachi
4	3/2/2020	1	0	0	Iran/Taftan	Gilgit-Baltistan	Gilgit

Display last Five Records of dataset

CVD.tail()

	Date	Cases	Deaths	Recovered	Travel_history	Province	City
2793	5/30/2020	952	36	394	NaN	Punjab	Lahore
2794	5/31/2020	1184	22	120	NaN	Punjab	Lahore
2795	6/1/2020	1610	43	95	NaN	Punjab	Lahore
2796	6/2/2020	1639	30	353	NaN	Punjab	Lahore
2797	6/3/2020	1615	37	243	NaN	Punjab	Lahore

CVD

	Date	Cases	Deaths	Recovered	Travel_history	Province	City
0	2/26/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
1	2/26/2020	2	0	0	Iran/Taftan	Sindh	Karachi
2	2/29/2020	1	0	0	China	Islamabad Capital Territory	Islamabad
3	2/29/2020	1	0	0	Iran/Taftan	Sindh	Karachi
4	3/2/2020	1	0	0	Iran/Taftan	Gilgit-Baltistan	Gilgit
...	...	...	...	...	...	...	...
2793	5/30/2020	952	36	394	NaN	Punjab	Lahore
2794	5/31/2020	1184	22	120	NaN	Punjab	Lahore
2795	6/1/2020	1610	43	95	NaN	Punjab	Lahore
2796	6/2/2020	1639	30	353	NaN	Punjab	Lahore
2797	6/3/2020	1615	37	243	NaN	Punjab	Lahore

2798 rows × 7 columns

Data Statistics

To display total rows and columns

CVD.shape

(2798, 7)

My dataset consists of 2798 rows and 8 columns.

CVD.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2798 entries, 0 to 2797
Data columns (total 7 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Date            2798 non-null   object
 1   Cases           2798 non-null   int64 
 2   Deaths          2798 non-null   int64 
 3   Recovered       2798 non-null   int64 
 4   Travel_history  2762 non-null   object
 5   Province        2798 non-null   object
 6   City            2798 non-null   object
dtypes: int64(3), object(4)
memory usage: 153.1+ KB

To show Datatypes of all columns

CVD.dtypes

Date              object
Cases              int64
Deaths             int64
Recovered          int64
Travel_history    object
Province          object
City              object
dtype: object

Statistics of numerical columns

CVD.describe()

	Cases	Deaths	Recovered
count	2798.000000	2798.000000	2798.000000
mean	30.016440	0.617584	8.847034
std	128.861666	2.716284	61.362566
min	0.000000	0.000000	-2.000000
25%	0.000000	0.000000	0.000000
50%	2.000000	0.000000	0.000000
75%	9.000000	0.000000	1.000000
max	1639.000000	43.000000	1431.000000

Statistics of Categorical columns

CVD.describe(include="object")

	Date	Travel_history	Province	City
count	2798	2762	2798	2798
unique	91	15	10	132
top	5/30/2020	Local - Social Contact	Khyber Pakhtunkhwa	Karachi
freq	72	2499	1489	95

To show unique dates,city and provinces

dates = CVD['Date'].unique()
dates

array(['2/26/2020', '2/29/2020', '3/2/2020', '3/6/2020', '3/7/2020',
       '3/9/2020', '3/10/2020', '3/11/2020', '3/12/2020', '3/13/2020',
       '3/15/2020', '3/16/2020', '3/17/2020', '3/18/2020', '3/19/2020',
       '3/20/2020', '3/21/2020', '3/22/2020', '3/23/2020', '3/24/2020',
       '3/25/2020', '3/26/2020', '3/27/2020', '3/28/2020', '3/29/2020',
       '3/30/2020', '3/31/2020', '4/1/2020', '4/2/2020', '4/3/2020',
       '4/4/2020', '4/5/2020', '4/6/2020', '4/7/2020', '4/8/2020',
       '4/9/2020', '4/10/2020', '4/11/2020', '4/12/2020', '4/13/2020',
       '4/14/2020', '4/15/2020', '4/16/2020', '4/17/2020', '4/18/2020',
       '4/19/2020', '4/20/2020', '4/21/2020', '4/22/2020', '4/23/2020',
       '4/24/2020', '4/25/2020', '4/26/2020', '4/27/2020', '4/28/2020',
       '4/29/2020', '4/30/2020', '5/1/2020', '5/2/2020', '5/3/2020',
       '5/4/2020', '5/5/2020', '5/6/2020', '5/7/2020', '5/8/2020',
       '5/9/2020', '5/10/2020', '5/11/2020', '5/12/2020', '5/13/2020',
       '5/14/2020', '5/15/2020', '5/16/2020', '5/17/2020', '5/18/2020',
       '5/19/2020', '5/20/2020', '5/21/2020', '5/22/2020', '5/23/2020',
       '5/24/2020', '5/25/2020', '5/26/2020', '5/27/2020', '5/28/2020',
       '5/29/2020', '5/30/2020', '5/31/2020', '6/1/2020', '6/2/2020',
       '6/3/2020'], dtype=object)

CVD['Province'].unique()

array(['Islamabad Capital Territory', 'Sindh', 'Gilgit-Baltistan',
       'Baluchistan', 'Punjab', 'Khyber Pakhtunkhwa',
       'khyber Pakhtunkhwa', 'Azad Jummu Kashmir',
       'Federal Administration Tribal Area',
       'islamabad Capital Territory'], dtype=object)

CVD['City'].unique()

array(['Islamabad', 'Karachi', 'Gilgit', 'Quetta', 'Hyderabad', 'Skardu',
       'Taftan', 'Lahore', 'Sukkur', 'Dera Ismail Khan', 'Mardan',
       'Mansehra', 'Peshawar', 'Charsadda', 'Dera Ghazi Khan', 'Mirpur',
       'Buner', 'Mardan ', 'Hangu ', 'Nagar', 'Multan', 'Khyber',
       'Rawalpindi', 'Jehlum', 'Gujrat ', 'Karak', 'Gujranwala', 'Dadu',
       'South Waziristan', 'Sargodha', 'Faisalabad', 'Mandi Bahauddin',
       'Rahim yar khan', 'Astore', 'Kharmang', 'Swabi', 'Gujrat',
       'Dir Upper', 'Swat', 'Mianwali', 'Narowal', 'Nankana', 'Attock',
       'Bahawal Nagar', 'Shigar', 'Shangla', 'Nowshera', 'Orakzai',
       'Malakand', 'Kohat', 'Larkana', 'Dir Lower', 'Abbottabad',
       'Khushab', 'Vehari', 'Raiwind', 'Muzaffarabad', 'Ghanche',
       'Bahawalpur', 'Rahim Yar Khan', 'Bajaur', 'Bannu', 'Kurrum',
       'Haripur', 'Hafizabad', 'Kasur', 'Laiya', 'Jacobabad', 'Bhambore ',
       'Trarkhel', 'Tank', 'Lodhran', 'Sialkot', 'Hangu', 'Chiniot',
       'Sheikhupura', 'Lakki Marwat', 'Torghar', 'North Waziristan',
       'Mohmand', 'Okara', 'Toba Tek Singh', 'Jamshoro', 'Badin',
       'Shaheed Benazirabad', 'Naushero Feroz', 'Shujawal', 'Sanghar',
       'Tando M. Khan', 'Ghotki', 'Chakwal', 'Jhang', 'Bhakhar',
       'Khairpur', 'Sujawal', 'Sahewal', 'Rajanpur', 'Muzaffargar',
       'Khanewal', 'Pakpatan', 'Diamer', 'Shikarpur', 'Tando Alahyar',
       'Umerkot', 'Kambar Shahdadkot', 'Kashmore', 'Mirpurkhas',
       'Tharparkar', 'Battagram', 'Thatta', 'Chitral Lower',
       'Chitral Upper', 'Kohistan upper', 'Kohistan Lower',
       'Kolai Pallas', 'Matiari', 'Kurram', 'ISlamabad', 'Pishin',
       'islamabad', 'Chitral', 'Kohistan Upper', 'Ziarat', 'Tor  Ghar',
       'Dira Ismail Khan', 'Hunza', 'Tor Ghar', 'Kolai Pllas', 'Ghizer',
       'Tando Allahyar', 'Mirpur Khas', 'Other'], dtype=object)

Data Preprocessing

Rename column name

CVD.rename(columns={'Cases':'Confirmed_cases','Deaths':'Deaths_cases',"Recovered":"Recovered_cases"},inplace=True)

Insert a column of active cases in dataset

CVD['Active_cases']=CVD['Confirmed_cases']-CVD["Deaths_cases"]-CVD["Recovered_cases"]

Dataframe after addition of active cases columns

CVD

	Date	Confirmed_cases	Deaths_cases	Recovered_cases	Travel_history	Province	City	Active_cases
0	2/26/2020	1	0	0	China	Islamabad Capital Territory	Islamabad	1
1	2/26/2020	2	0	0	Iran/Taftan	Sindh	Karachi	2
2	2/29/2020	1	0	0	China	Islamabad Capital Territory	Islamabad	1
3	2/29/2020	1	0	0	Iran/Taftan	Sindh	Karachi	1
4	3/2/2020	1	0	0	Iran/Taftan	Gilgit-Baltistan	Gilgit	1
...	...	...	...	...	...	...	...	...
2793	5/30/2020	952	36	394	NaN	Punjab	Lahore	522
2794	5/31/2020	1184	22	120	NaN	Punjab	Lahore	1042
2795	6/1/2020	1610	43	95	NaN	Punjab	Lahore	1472
2796	6/2/2020	1639	30	353	NaN	Punjab	Lahore	1256
2797	6/3/2020	1615	37	243	NaN	Punjab	Lahore	1335

2798 rows × 8 columns

indexNames = CVD[CVD['Active_cases'] <0 ].index
CVD.drop(indexNames , inplace=True)

CVD

	Date	Confirmed_cases	Deaths_cases	Recovered_cases	Travel_history	Province	City	Active_cases
0	2/26/2020	1	0	0	China	Islamabad Capital Territory	Islamabad	1
1	2/26/2020	2	0	0	Iran/Taftan	Sindh	Karachi	2
2	2/29/2020	1	0	0	China	Islamabad Capital Territory	Islamabad	1
3	2/29/2020	1	0	0	Iran/Taftan	Sindh	Karachi	1
4	3/2/2020	1	0	0	Iran/Taftan	Gilgit-Baltistan	Gilgit	1
...	...	...	...	...	...	...	...	...
2793	5/30/2020	952	36	394	NaN	Punjab	Lahore	522
2794	5/31/2020	1184	22	120	NaN	Punjab	Lahore	1042
2795	6/1/2020	1610	43	95	NaN	Punjab	Lahore	1472
2796	6/2/2020	1639	30	353	NaN	Punjab	Lahore	1256
2797	6/3/2020	1615	37	243	NaN	Punjab	Lahore	1335

2387 rows × 8 columns

Correct all the wrong(mismatch) province and city entries

CVD.loc[CVD['City']=='Mardan ','City']='Mardan'
CVD.loc[CVD['City']=='Hangu ','City']='Hangu'
CVD.loc[CVD['City']=='Gujrat ','City']='Gujrat'
CVD.loc[CVD['City']=='ISlamabad','City']='Islamabad'
CVD.loc[CVD['City']=='islamabad','City']='Islamabad'
CVD.loc[CVD['City']=='Rahim yar khan','City']='Rahim_Yar_Khan'
CVD.loc[CVD['City']=='Rahim Yar Khan','City']='Rahim_Yar_Khan'

CVD.loc[CVD['City']=='Shujawal','City']='Sujawal'
CVD.loc[CVD['City']=='Tando Allahyar','City']='Tando_Alahyar'
CVD.loc[CVD['City']=='Tando Alahyar','City']='Tando_Alahyar'

CVD.loc[CVD['City']=='Kurram','City']='Tando_Kurrum'
CVD.loc[CVD['City']=='Tor  Ghar','City']='Tor_Ghar'
CVD.loc[CVD['City']=='Tor Ghar','City']='Tor_Ghar'
CVD.loc[CVD['City']=='Kolai Pallas','City']='Kolai_Pallas'

CVD.loc[CVD['City']=='Kolai Pllas','City']='Kolai_Pallas'
CVD.loc[CVD['City']=='Mirpurkhas','City']='Mirpur_Khas'
CVD.loc[CVD['City']=='Mirpur Khas','City']='Mirpur_Khas'

CVD.loc[CVD['City']=='Torghar','City']='Tor_Ghar'
CVD.loc[CVD['City']=='Dera Ismail Khan','City']='Dera_Ismail_Khan'
CVD.loc[CVD['City']=='South Waziristan','City']='South_Waziristan'
CVD.loc[CVD['City']=='Mandi Bahauddin','City']='Mandi_Bahauddin'
CVD.loc[CVD['City']=='Dir Upper','City']='Dir_Upper'
CVD.loc[CVD['City']=='Dir Lower','City']='Dir_Lower'
CVD.loc[CVD['City']=='Lakki Marwat','City']='Lakki_Marwat'
CVD.loc[CVD['City']=='North Waziristan','City']='North_Waziristan'
CVD.loc[CVD['City']=='Toba Tek Singh','City']='Toba_Tek_Singh'
CVD.loc[CVD['City']=='Shaheed Benazirabad','City']='Shaheed_Benazirabad'
CVD.loc[CVD['City']=='Naushero Feroz','City']='Naushero_Feroz'
CVD.loc[CVD['City']=='Tando M. Khan','City']='Tando_M.Khan'
CVD.loc[CVD['City']=='Kambar Shahdadkot','City']='Kambar_Shahdadkot'
CVD.loc[CVD['City']=='Kohistan upper','City']='Kohistan_upper'
CVD.loc[CVD['City']=='Kohistan Lower','City']='Kohistan_Lower'
CVD.loc[CVD['City']=='Chitral Upper','City']='Chitral_Upper'
CVD.loc[CVD['City']=='Chitral Lower','City']='Chitral_Lower'
CVD.loc[CVD['City']=='Kohistan Upper','City']='Kohistan_Upper'
CVD.loc[CVD['City']=='Dira Ismail Khan','City']='Dira_Ismail_Khan'
CVD.loc[CVD['City']=='Dera Ghazi Khan','City']='Dera_Ghazi_Khan'
CVD.loc[CVD['City']=='Bahawal Nagar','City']='Bahawal_Nagar'
CVD.loc[CVD['City']=='Dira_Ismail_Khan','City']='Dera_Ismail_Khan'   
CVD.loc[CVD['City']=='Kohistan_upper','City']='Kohistan_Upper'
CVD.loc[CVD['City']=='Tando_M.Khan','City']='Tando_M'
CVD.loc[CVD['Province']=='Khyber Pakhtunkhwa','Province']='Khyber_Pakhtunkhwa'

CVD.loc[CVD['Province']=='khyber Pakhtunkhwa','Province']='Khyber_Pakhtunkhwa'
CVD.loc[CVD['Province']=='Gilgit-Baltistan','Province']='Gilgit_Baltistan'
CVD.loc[CVD['Province']=='Islamabad Capital Territory','Province']='Islamabad_Capital_Territory'
CVD.loc[CVD['Province']=='islamabad Capital Territory','Province']='Islamabad_Capital_Territory'

CVD.loc[CVD['Province']=='Azad Jummu Kashmir','Province']='Azad_Jummu_Kashmir'
CVD.loc[CVD['Province']=='Federal Administration Tribal Area','Province']='Federal_Administration_Tribal_Area'
CVD.loc[CVD['Travel_history']=='Local - Social Contact','Travel_history']='Local_Social_Contact'

CVD.loc[CVD['Travel_history']=='Iran/Taftan','Travel_history']='Iran_Taftan'
CVD.loc[CVD['Travel_history']=='Local - Covid Relative','Travel_history']='Local_Covid_Relative'
CVD.loc[CVD['Travel_history']=='Tableeghi Jamaat','Travel_history']='Tableeghi_Jamaat'
CVD.loc[CVD['Travel_history']=='International Passenger','Travel_history']='International_Passenger'

Display Correct City and Province names

CVD['Province'].unique()

array(['Islamabad_Capital_Territory', 'Sindh', 'Gilgit_Baltistan',
       'Baluchistan', 'Punjab', 'Khyber_Pakhtunkhwa',
       'Azad_Jummu_Kashmir', 'Federal_Administration_Tribal_Area'],
      dtype=object)

CVD['City'].unique()

array(['Islamabad', 'Karachi', 'Gilgit', 'Quetta', 'Hyderabad', 'Skardu',
       'Taftan', 'Lahore', 'Sukkur', 'Dera_Ismail_Khan', 'Mardan',
       'Mansehra', 'Peshawar', 'Charsadda', 'Dera_Ghazi_Khan', 'Mirpur',
       'Buner', 'Hangu', 'Nagar', 'Multan', 'Khyber', 'Rawalpindi',
       'Jehlum', 'Gujrat', 'Karak', 'Gujranwala', 'Dadu',
       'South_Waziristan', 'Sargodha', 'Faisalabad', 'Mandi_Bahauddin',
       'Rahim_Yar_Khan', 'Astore', 'Kharmang', 'Swabi', 'Dir_Upper',
       'Swat', 'Mianwali', 'Narowal', 'Nankana', 'Attock',
       'Bahawal_Nagar', 'Shigar', 'Shangla', 'Nowshera', 'Orakzai',
       'Malakand', 'Kohat', 'Larkana', 'Dir_Lower', 'Abbottabad',
       'Khushab', 'Vehari', 'Raiwind', 'Muzaffarabad', 'Ghanche',
       'Bahawalpur', 'Bajaur', 'Bannu', 'Kurrum', 'Haripur', 'Hafizabad',
       'Kasur', 'Laiya', 'Jacobabad', 'Bhambore ', 'Trarkhel', 'Tank',
       'Lodhran', 'Sialkot', 'Chiniot', 'Sheikhupura', 'Lakki_Marwat',
       'Tor_Ghar', 'North_Waziristan', 'Mohmand', 'Okara',
       'Toba_Tek_Singh', 'Jamshoro', 'Badin', 'Shaheed_Benazirabad',
       'Naushero_Feroz', 'Sujawal', 'Sanghar', 'Tando_M', 'Ghotki',
       'Chakwal', 'Jhang', 'Bhakhar', 'Khairpur', 'Sahewal', 'Rajanpur',
       'Muzaffargar', 'Khanewal', 'Pakpatan', 'Diamer', 'Shikarpur',
       'Tando_Alahyar', 'Umerkot', 'Kambar_Shahdadkot', 'Kashmore',
       'Mirpur_Khas', 'Tharparkar', 'Battagram', 'Thatta',
       'Chitral_Lower', 'Chitral_Upper', 'Kohistan_Upper',
       'Kohistan_Lower', 'Kolai_Pallas', 'Matiari', 'Tando_Kurrum',
       'Pishin', 'Chitral', 'Ziarat', 'Hunza', 'Ghizer', 'Other'],
      dtype=object)

CVD['Travel_history'].unique()

array(['China', 'Iran_Taftan', 'Syria', 'UK', 'Local_Social_Contact',
       'USA', 'Unknown', 'KSA', 'Local_Covid_Relative', 'Dubai',
       'International_Passenger', 'Tableeghi_Jamaat', 'Jail',
       'Afghanistan', nan], dtype=object)

Change datatype of Date column

CVD['Date'] =pd.to_datetime(CVD['Date'], infer_datetime_format=True)

Show the starting, ending date and total days of our dataset

print(f"Earliest Entry: {CVD['Date'].min()}")
print(f"Last Entry:     {CVD['Date'].max()}")
print(f"Total Days:     {CVD['Date'].max()-CVD['Date'].min()}")

Earliest Entry: 2020-02-26 00:00:00
Last Entry:     2020-06-03 00:00:00
Total Days:     98 days 00:00:00

Change the Datatype of Date column

CVD['Date'] =pd.to_datetime(CVD['Date'], infer_datetime_format=True)

CVD.dtypes

Date               datetime64[ns]
Confirmed_cases             int64
Deaths_cases                int64
Recovered_cases             int64
Travel_history             object
Province                   object
City                       object
Active_cases                int64
dtype: object

Determine null values

CVD.isnull().sum()

Date                0
Confirmed_cases     0
Deaths_cases        0
Recovered_cases     0
Travel_history     31
Province            0
City                0
Active_cases        0
dtype: int64

Above analysis show that my dataset contain null values so much be removed or can be repalce with mean median or nmode also..

Drop those rows which consist of any null values

CVD.dropna(axis=0, inplace=True)

Show null values remove from dataframe or not

CVD.isnull().sum()

Date               0
Confirmed_cases    0
Deaths_cases       0
Recovered_cases    0
Travel_history     0
Province           0
City               0
Active_cases       0
dtype: int64

This show that now our dataset does not contain any null value.

Determine if data has any dulpicated value

CVD.duplicated(subset=None,keep='first').sum()

1

duplicateRows = CVD[CVD.duplicated()]
print("Duplicate Rows except first occurrence based on all columns are :")
print(duplicateRows)

Duplicate Rows except first occurrence based on all columns are :
           Date  Confirmed_cases  Deaths_cases  Recovered_cases  \
1560 2020-05-13                1             0                0   

            Travel_history          Province    City  Active_cases  
1560  Local_Social_Contact  Gilgit_Baltistan  Ghizer             1  

Drop duplicates rows

CVD.drop_duplicates( keep = False, inplace = True)
CVD

	Date	Confirmed_cases	Deaths_cases	Recovered_cases	Travel_history	Province	City	Active_cases
0	2020-02-26	1	0	0	China	Islamabad_Capital_Territory	Islamabad	1
1	2020-02-26	2	0	0	Iran_Taftan	Sindh	Karachi	2
2	2020-02-29	1	0	0	China	Islamabad_Capital_Territory	Islamabad	1
3	2020-02-29	1	0	0	Iran_Taftan	Sindh	Karachi	1
4	2020-03-02	1	0	0	Iran_Taftan	Gilgit_Baltistan	Gilgit	1
...	...	...	...	...	...	...	...	...
2754	2020-05-27	5	1	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	4
2755	2020-05-28	8	0	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	8
2756	2020-05-29	7	1	6	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	0
2759	2020-06-01	16	0	2	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	14
2760	2020-06-02	13	1	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	12

2354 rows × 8 columns

CVD.duplicated(subset=None,keep='first').sum()

0

CVD['Travel_history'].unique()

array(['China', 'Iran_Taftan', 'Syria', 'UK', 'Local_Social_Contact',
       'USA', 'Unknown', 'KSA', 'Local_Covid_Relative', 'Dubai',
       'International_Passenger', 'Tableeghi_Jamaat', 'Jail',
       'Afghanistan'], dtype=object)

CVD.to_csv("covid2.csv")
CVD.to_excel("covid2.xls")

Now our data does not contain any duplicate value

now our data is cleaned

Data Analysis and Visualization

We will analyze some insights from our datsets in form of visualization

CVD

	Date	Confirmed_cases	Deaths_cases	Recovered_cases	Travel_history	Province	City	Active_cases
0	2020-02-26	1	0	0	China	Islamabad_Capital_Territory	Islamabad	1
1	2020-02-26	2	0	0	Iran_Taftan	Sindh	Karachi	2
2	2020-02-29	1	0	0	China	Islamabad_Capital_Territory	Islamabad	1
3	2020-02-29	1	0	0	Iran_Taftan	Sindh	Karachi	1
4	2020-03-02	1	0	0	Iran_Taftan	Gilgit_Baltistan	Gilgit	1
...	...	...	...	...	...	...	...	...
2754	2020-05-27	5	1	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	4
2755	2020-05-28	8	0	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	8
2756	2020-05-29	7	1	6	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	0
2759	2020-06-01	16	0	2	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	14
2760	2020-06-02	13	1	0	Local_Social_Contact	Azad_Jummu_Kashmir	Muzaffarabad	12

2354 rows × 8 columns

1. Visualization of numerical columns of dataset

CVD.hist(figsize=(8,8))

array([[<AxesSubplot:title={'center':'Confirmed_cases'}>,
        <AxesSubplot:title={'center':'Deaths_cases'}>],
       [<AxesSubplot:title={'center':'Recovered_cases'}>,
        <AxesSubplot:title={'center':'Active_cases'}>]], dtype=object)

2. Show the cities with confirmed cases and determine how many cities have more than 1 case,100 case and thousand case.

target_date = CVD['Date'].max()

print('Date: ', target_date)
for i in [1, 10, 100, 1000, 10000]:
    n_City = len(CVD.query('(Date == @target_date) & Confirmed_cases > @i'))
    print(f'{n_City} cities have more than {i} confirmed cases')

Date:  2020-06-03 00:00:00
43 cities have more than 1 confirmed cases
22 cities have more than 10 confirmed cases
3 cities have more than 100 confirmed cases
1 cities have more than 1000 confirmed cases
0 cities have more than 10000 confirmed cases

3).Show the Confirmed, Active,Death and Recovered cases of each province in one table.

Numerical visualization

world=CVD.groupby('Province')[['Confirmed_cases','Active_cases','Deaths_cases','Recovered_cases']].sum().reset_index()
world

	Province	Confirmed_cases	Active_cases	Deaths_cases	Recovered_cases
0	Azad_Jummu_Kashmir	248	217	6	25
1	Baluchistan	2389	2127	30	232
2	Federal_Administration_Tribal_Area	52	49	0	3
3	Gilgit_Baltistan	758	688	5	65
4	Islamabad_Capital_Territory	3522	3029	36	457
5	Khyber_Pakhtunkhwa	9963	8591	407	965
6	Punjab	14574	13515	227	832
7	Sindh	29762	19686	477	9599

Graphical visualization

world.plot.bar(x="Province")
plt.gcf().set_size_inches(15, 6)
plt.show()

Show the Confirmed, Active,Death and Recovered cases of each Travel_history in one table.

world=CVD.groupby('Travel_history')[['Confirmed_cases','Active_cases','Deaths_cases','Recovered_cases']].sum().reset_index()
world

	Travel_history	Confirmed_cases	Active_cases	Deaths_cases	Recovered_cases
0	Afghanistan	70	70	0	0
1	China	2	2	0	0
2	Dubai	1	1	0	0
3	International_Passenger	38	38	0	0
4	Iran_Taftan	1035	1025	6	4
5	Jail	107	107	0	0
6	KSA	6	5	1	0
7	Local_Covid_Relative	1	1	0	0
8	Local_Social_Contact	55449	42456	1103	11890
9	Syria	7	7	0	0
10	Tableeghi_Jamaat	1093	1084	2	7
11	UK	5	5	0	0
12	USA	1	1	0	0
13	Unknown	3453	3100	76	277

world.plot.bar(x="Travel_history")
plt.xlabel("Travel_history",fontsize = 20)
plt.xticks(rotation = 85 ,fontsize = 15)
plt.ylabel('Cases',fontsize = 20)
plt.title("Cases depends on Travel history " , fontsize = 30)
plt.gcf().set_size_inches(15, 6)
plt.show()

5). Show the sum of cases of all province And determine which province face most confirmed cases

* Tablewise visualization

query_2 ='''SELECT Province,Sum("Confirmed_cases")as Confirmed_cases  FROM CVD GROUP BY Province;'''
res_3 = pysqldf(query_2)
res_3

	Province	Confirmed_cases
0	Azad_Jummu_Kashmir	248
1	Baluchistan	2389
2	Federal_Administration_Tribal_Area	52
3	Gilgit_Baltistan	758
4	Islamabad_Capital_Territory	3522
5	Khyber_Pakhtunkhwa	9963
6	Punjab	14574
7	Sindh	29762

* Graph Visualization

fig = px.pie(CVD,
             values="Confirmed_cases",
             names="Province",
             title="Confirmed cases",
             template="seaborn")
fig.update_traces(rotation=90, pull=0.05, textinfo='value+label')
fig.show()

Above graph show that sindh has most confirmed cases

6). Show by dataframe provincewise active covid cases and determine which province have most active cases

* Numerical interpretation

query_2 ='''SELECT Province,Sum("Active_cases")as Active_cases  FROM CVD GROUP BY Province;'''
res_3 = pysqldf(query_2)
res_3

	Province	Active_cases
0	Azad_Jummu_Kashmir	217
1	Baluchistan	2127
2	Federal_Administration_Tribal_Area	49
3	Gilgit_Baltistan	688
4	Islamabad_Capital_Territory	3029
5	Khyber_Pakhtunkhwa	8591
6	Punjab	13515
7	Sindh	19686

*Graphical visualization

fig = px.pie(CVD,
             values="Active_cases",
             names="Province",
             title="Active cases",
             template="seaborn")
fig.update_traces(rotation=90, pull=0.05, textinfo='value+label')
fig.show()

Aove graph and table show that sidhh have most active cases and punjab also has most active cases but minimum to sindh.

7). Show by table provincewise Deaths covid cases and determine which province have most Deaths cases

Tablewise Visualization

query_2 ='''SELECT Province,Sum("Deaths_cases")as Deaths_cases  FROM CVD GROUP BY Province;'''
res_3 = pysqldf(query_2)
res_3

	Province	Deaths_cases
0	Azad_Jummu_Kashmir	6
1	Baluchistan	30
2	Federal_Administration_Tribal_Area	0
3	Gilgit_Baltistan	5
4	Islamabad_Capital_Territory	36
5	Khyber_Pakhtunkhwa	407
6	Punjab	227
7	Sindh	477

Graphical visualization

fig = px.pie(CVD,
             values="Deaths_cases",
             names="Province",
             title="Deaths cases",
             template="seaborn")
fig.update_traces(rotation=90, pull=0.05, textinfo='value+label')
fig.show()

8). Show by table provincewise REcovered covid cases and determine which province have most Recovered cases

Numerical visualization

query_2 ='''SELECT Province,Sum("Recovered_cases")as Recovered_cases  FROM CVD GROUP BY Province;'''
res_3 = pysqldf(query_2)
res_3

	Province	Recovered_cases
0	Azad_Jummu_Kashmir	25
1	Baluchistan	232
2	Federal_Administration_Tribal_Area	3
3	Gilgit_Baltistan	65
4	Islamabad_Capital_Territory	457
5	Khyber_Pakhtunkhwa	965
6	Punjab	832
7	Sindh	9599

Graphical visualization

fig = px.pie(CVD,
             values="Recovered_cases",
             names="Province",
             title="Recovered cases",
             template="seaborn")
fig.update_traces(rotation=90, pull=0.05, textinfo='value+label')
fig.show()

9). Show that with passage of date and month whether this disease(COVID_19) increase or decrease.

plt.figure(figsize= (40,15))
plt.xticks(rotation = 90 ,fontsize = 40)
plt.yticks(fontsize =25)
plt.xlabel("Dates",fontsize = 40)
plt.ylabel('Total cases',fontsize = 40)
plt.title(" Cofirmed Over Time" , fontsize = 50)
total_cases = CVD.groupby('Date')[['Date', 'Confirmed_cases']].sum().reset_index()
total_cases['Date'] = pd.to_datetime(total_cases['Date'])
total_cases['Date']
ax = sns.lineplot( x = total_cases.Date,  y = total_cases.Confirmed_cases , color = 'r')

This show that with time covid disease also increase

NOW WE ANALYZE THE CONDITION OF COVID _19 IN CITIES

10) . Show top 10 cities having most Confirmed cases and determine which city has more cases.

Numerical visualization

 top=CVD[CVD['Date'] ==CVD['Date']]
top_casualities = top.groupby(by = 'City')['Confirmed_cases'].sum().sort_values(ascending = False).head(10).reset_index()
top_casualities

	City	Confirmed_cases
0	Karachi	26685
1	Lahore	11493
2	Peshawar	4909
3	Islamabad	3522
4	Quetta	2373
5	Swat	887
6	Multan	712
7	Sukkur	630
8	Raiwind	546
9	Hyderabad	527

Graphical visualization

plt.figure(figsize= (10,5))
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel("Total cases",fontsize = 30)
plt.ylabel('City',fontsize = 30)
plt.title("Top 10 cities having most confirmed cases" , fontsize = 30)
ax = sns.barplot(x = top_casualities.Confirmed_cases, y = top_casualities.City)
for i, (value, name) in enumerate(zip(top_casualities.Confirmed_cases,top_casualities.City)):
    ax.text(value, i-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(xlabel='Total confirmed  cases', ylabel='City')

[Text(0.5, 0, 'Total confirmed  cases'), Text(0, 0.5, 'City')]

Above graph and table show that karachi has most confirmed cases

11). Show top 10 cities having most active cases

Numerical visualization

active=CVD[CVD['Date'] ==CVD['Date']]
active_casualities =active.groupby(by = 'City')['Active_cases'].sum().sort_values(ascending = False).head(10).reset_index()
active_casualities

	City	Active_cases
0	Karachi	17114
1	Lahore	10447
2	Peshawar	4313
3	Islamabad	3029
4	Quetta	2111
5	Multan	709
6	Swat	699
7	Raiwind	544
8	Sukkur	532
9	Malakand	384

Graphical visualization.

plt.figure(figsize= (10,5))
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel("Total cases",fontsize = 30)
plt.ylabel('City',fontsize = 30)
plt.title("Top 10 cities having most Active cases" , fontsize = 30)
ax = sns.barplot(x = active_casualities.Active_cases, y = active_casualities.City)
for i, (value, name) in enumerate(zip(active_casualities.Active_cases,active_casualities.City)):
    ax.text(value, i-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(xlabel='Total cases', ylabel='City')

[Text(0.5, 0, 'Total cases'), Text(0, 0.5, 'City')]

12). Determine top 10 countries having most deaths cases

Numerical visualization

death=CVD[CVD['Date'] ==CVD['Date']]
death_casualities = death.groupby(by = 'City')['Deaths_cases'].sum().sort_values(ascending = False).head(10).reset_index()
death_casualities

	City	Deaths_cases
0	Karachi	455
1	Peshawar	252
2	Lahore	219
3	Swat	37
4	Islamabad	36
5	Quetta	30
6	Mardan	22
7	Malakand	13
8	Abbottabad	12
9	Nowshera	11

Graphical visualization

plt.figure(figsize= (10,7))
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel("Total Deaths",fontsize = 30)
plt.ylabel('City',fontsize = 30)
plt.title("Top 10 cities having most deaths cases" , fontsize = 20)
ax = sns.barplot(x = death_casualities.Deaths_cases, y =death_casualities.City)
for i, (value, name) in enumerate(zip(death_casualities.Deaths_cases,death_casualities.City)):
    ax.text(value, i-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(xlabel='Total Deaths', ylabel='City')

[Text(0.5, 0, 'Total Deaths'), Text(0, 0.5, 'City')]

13). Show that 10 countries having more Recovered cases

Numerical visualization

Recovered=CVD[CVD['Date'] ==CVD['Date']]
Recovered_casualities = Recovered.groupby(by = 'City')['Recovered_cases'].sum().sort_values(ascending = False).head(10).reset_index()
Recovered_casualities

	City	Recovered_cases
0	Karachi	9116
1	Lahore	827
2	Islamabad	457
3	Peshawar	344
4	Quetta	232
5	Swat	151
6	Hyderabad	143
7	Sukkur	96
8	Malakand	66
9	Ghotki	56

Graphical visualization.

plt.figure(figsize= (10,7))
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel("Total Recovered",fontsize = 20)
plt.ylabel('City',fontsize = 30)
plt.title("Top 10 cities having most recovered cases" , fontsize = 30)
ax = sns.barplot(x = Recovered_casualities.Recovered_cases, y =Recovered_casualities.City)
for i, (value, name) in enumerate(zip(Recovered_casualities.Recovered_cases,Recovered_casualities.City)):
    ax.text(value, i-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(xlabel='Total Recovered cases', ylabel='City')

[Text(0.5, 0, 'Total Recovered cases'), Text(0, 0.5, 'City')]

Above 4 graphs shows that (KARACHI) have most confirmed,Active,Recovered and Deaths Cases

14). Show that mortality rate of each Province and determine which Province have greater mortality rate.

Numerical visualization

rate =CVD.groupby(by = 'Province')[['Recovered_cases','Confirmed_cases','Deaths_cases']].sum().reset_index()
rate['Recovery percentage'] =  round(((rate['Recovered_cases']) / (rate['Confirmed_cases'])) * 100 , 2)
rate['Death percentage'] =  round(((rate['Deaths_cases']) / (rate['Confirmed_cases'])) * 100 , 2)
rate.head()

	Province	Recovered_cases	Confirmed_cases	Deaths_cases	Recovery percentage	Death percentage
0	Azad_Jummu_Kashmir	25	248	6	10.08	2.42
1	Baluchistan	232	2389	30	9.71	1.26
2	Federal_Administration_Tribal_Area	3	52	0	5.77	0.00
3	Gilgit_Baltistan	65	758	5	8.58	0.66
4	Islamabad_Capital_Territory	457	3522	36	12.98	1.02

Graphical visualization

plt.figure(figsize= (20,7))
plt.xticks(rotation=70,fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylabel("Total cases",fontsize = 15)
plt.xlabel('Province',fontsize = 30)
plt.title("Province having  mortality rate" , fontsize = 30)
ax = sns.pointplot(y = rate['Death percentage'], x= rate.Province)
for i, (value, name) in enumerate(zip(rate['Death percentage'], rate.Province)):
    ax.text(value,-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(ylabel='Mortality Rate in percentage', xlabel='Province')

[Text(0, 0.5, 'Mortality Rate in percentage'), Text(0.5, 0, 'Province')]

Above Graph show that (KHYBER_PAKHTUNKHWA) have greater mortality rate.

top=CVD[CVD['Date'] ==CVD['Date']]
top_casualities = top.groupby(by = 'Travel_history')['Confirmed_cases'].sum().sort_values(ascending = False).reset_index()
top_casualities

	Travel_history	Confirmed_cases
0	Local_Social_Contact	55449
1	Unknown	3453
2	Tableeghi_Jamaat	1093
3	Iran_Taftan	1035
4	Jail	107
5	Afghanistan	70
6	International_Passenger	38
7	Syria	7
8	KSA	6
9	UK	5
10	China	2
11	USA	1
12	Local_Covid_Relative	1
13	Dubai	1

plt.figure(figsize= (25,15))
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel("Total Confirmed",fontsize = 30)
plt.ylabel('Travel_history',fontsize = 30)
plt.title("Top 10 cities having most deaths cases" , fontsize = 20)
ax = sns.pointplot(x = top_casualities.Confirmed_cases, y =top_casualities.Travel_history)
for i, (value, name) in enumerate(zip(top_casualities.Confirmed_cases,top_casualities.Travel_history)):
    ax.text(value, i-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(xlabel='Total Confirmed Cases', ylabel='Travel_history')

[Text(0.5, 0, 'Total Confirmed Cases'), Text(0, 0.5, 'Travel_history')]

15. Show Cities having mortality rate and determine which city have more mortality rate

Numerical Visulaization

rate =CVD.groupby(by = 'City')[['Recovered_cases','Confirmed_cases','Deaths_cases']].sum().reset_index()
rate['Recovery percentage'] =  round(((rate['Recovered_cases']) / (rate['Confirmed_cases'])) * 100 , 2)
rate['Death percentage'] =  round(((rate['Deaths_cases']) / (rate['Confirmed_cases'])) * 100 , 2)
rate.head()

	City	Recovered_cases	Confirmed_cases	Deaths_cases	Recovery percentage	Death percentage
0	Abbottabad	29	277	12	10.47	4.33
1	Astore	7	69	1	10.14	1.45
2	Attock	0	15	0	0.00	0.00
3	Badin	3	56	1	5.36	1.79
4	Bahawal_Nagar	0	21	0	0.00	0.00

Graphical Visualization

plt.figure(figsize= (50,20))
plt.xticks(rotation=90,fontsize = 25)
plt.yticks(fontsize = 40)
plt.ylabel("Total cases",fontsize = 45)
plt.xlabel('City',fontsize = 40)
plt.title(" Cities having  mortality rate" , fontsize = 40)
ax = sns.pointplot(x=rate.City,y = rate['Death percentage'])

for i, (value, name) in enumerate(zip(rate['Death percentage'] ,rate.City)):
    ax.text(value,-.05, f'{value:,.0f}',  size=10, ha='left',  va='center')
ax.set(ylabel='Mortality Rate in percentage', xlabel='Cities')

[Text(0, 0.5, 'Mortality Rate in percentage'), Text(0.5, 0, 'Cities')]

posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values

above graph show kohat has high mortality rate

16). Show that which province of country has most cases

Numercial Visualization

world=CVD.groupby('Province')[['Confirmed_cases','Active_cases','Deaths_cases','Recovered_cases']].sum().reset_index()
world

	Province	Confirmed_cases	Active_cases	Deaths_cases	Recovered_cases
0	Azad_Jummu_Kashmir	248	217	6	25
1	Baluchistan	2389	2127	30	232
2	Federal_Administration_Tribal_Area	52	49	0	3
3	Gilgit_Baltistan	758	688	5	65
4	Islamabad_Capital_Territory	3522	3029	36	457
5	Khyber_Pakhtunkhwa	9963	8591	407	965
6	Punjab	14574	13515	227	832
7	Sindh	29762	19686	477	9599

fig = px.line(world.sort_values('Active_cases', ascending=False)[:10][::-1], 
             x='Province', y='Active_cases', color_discrete_sequence=['#D63230'],
             title='Active cases', text='Active_cases', orientation='h')
fig.show()

The above graph show that sindh has more active,death cases now analyze sidth province ad determine in which cities corona is affected more

17). Determine which city in sindh is more affected by covid

Numerical visualization

wrld=CVD[CVD['Province']=='Sindh']
world=wrld.groupby('City')[['Confirmed_cases','Active_cases','Deaths_cases','Recovered_cases']].sum().reset_index()
world

	City	Confirmed_cases	Active_cases	Deaths_cases	Recovered_cases
0	Badin	56	52	1	3
1	Dadu	48	37	0	11
2	Ghotki	356	300	0	56
3	Hyderabad	527	378	6	143
4	Jacobabad	60	53	0	7
5	Jamshoro	87	72	0	15
6	Kambar_Shahdadkot	48	33	0	15
7	Karachi	26685	17114	455	9116
8	Kashmore	40	38	1	1
9	Khairpur	276	262	2	12
10	Larkana	303	272	3	28
11	Matiari	6	6	0	0
12	Mirpur_Khas	44	26	1	17
13	Naushero_Feroz	41	30	1	10
14	Other	48	44	2	2
15	Sanghar	57	53	0	4
16	Shaheed_Benazirabad	153	150	1	2
17	Shikarpur	160	113	1	46
18	Sujawal	32	32	0	0
19	Sukkur	630	532	2	96
20	Tando_Alahyar	7	8	0	-1
21	Tando_M	34	23	0	11
22	Tharparkar	7	6	0	1
23	Thatta	48	42	1	5
24	Umerkot	9	10	0	-1

Graphical visualization

fig = px.line(world.sort_values('Active_cases', ascending=False)[:10][::-1], 
             x='City', y='Active_cases', color_discrete_sequence=['#D63230'],
             title='Affected cities in sindh', text='Active_cases', orientation='h')
fig.show()

Above graph shows that karachi is more affected city in pakistan

18. Show Recovery cases and Deaths cases per 100 Confirmed Rate and determine which province has most recovery cases and least death cases..

cleaned_latest = CVD[CVD['Date'] == max(CVD['Date'])]
flg = cleaned_latest.groupby('Province')[['Confirmed_cases', 'Deaths_cases', 'Recovered_cases']].sum().reset_index()
flg['mortalityRate'] = round((flg['Deaths_cases']/flg['Confirmed_cases'])*100, 2)

print("Province with Lowest Mortality Rates")
temp = flg[flg['Confirmed_cases']>100]
temp = temp.sort_values('mortalityRate', ascending=True)[['Confirmed_cases', 'Province','Deaths_cases']][:16]
temp.sort_values('Confirmed_cases', ascending=False)[['Province', 'Confirmed_cases','Deaths_cases']][:20].style.background_gradient(cmap='Greens')

Province with Lowest Mortality Rates

	Province	Confirmed_cases	Deaths_cases
3	Sindh	1824	29
2	Khyber_Pakhtunkhwa	458	10
1	Islamabad_Capital_Territory	356	4

19. RECOVERY PER 100 CONFIRMED RATE

flg['RecoveryRate'] = round((flg['Recovered_cases']/flg['Confirmed_cases'])*100, 2)
temp = flg[flg['Confirmed_cases']>100]
temp = temp.sort_values('RecoveryRate', ascending=False)

fig = px.bar(temp.sort_values(by="RecoveryRate", ascending=False)[:10][::-1],
             x = 'RecoveryRate', y = 'Province', 
             title='Recoveries per 100 Confirmed Cases', text='RecoveryRate', height=500, orientation='h',
             color_discrete_sequence=['#2c1a9c']
            )
fig.show()

20)- DEATH PER 100 CONFIRMED RATE

flg['DeathsRate'] = round((flg['Deaths_cases']/flg['Confirmed_cases'])*100, 2)
temp = flg[flg['Confirmed_cases']>100]
temp = temp.sort_values('DeathsRate', ascending=False)

temp

	Province	Confirmed_cases	Deaths_cases	Recovered_cases	mortalityRate	RecoveryRate	DeathsRate
2	Khyber_Pakhtunkhwa	458	10	57	2.18	12.45	2.18
3	Sindh	1824	29	483	1.59	26.48	1.59
1	Islamabad_Capital_Territory	356	4	119	1.12	33.43	1.12

fig = px.bar(temp.sort_values(by="DeathsRate", ascending=False)[:10][::-1],
             x = 'DeathsRate', y = 'Province', 
             title='Deaths per 100 Confirmed Cases', text='DeathsRate', height=500, orientation='h',
             color_discrete_sequence=['#2aa19a']
            )
fig.show()

Above graphs show that in per 100 confirmed cases, patients recovery in Islamabad Territory is more than other province. And patients deaths in khyber pakhtunkhwa is more than any other province

Correlation

Corelation = CVD.corr()
Corelation

	Confirmed_cases	Deaths_cases	Recovered_cases	Active_cases
Confirmed_cases	1.000000	0.775857	0.717063	0.918170
Deaths_cases	0.775857	1.000000	0.593583	0.680828
Recovered_cases	0.717063	0.593583	1.000000	0.382481
Active_cases	0.918170	0.680828	0.382481	1.000000

top_fatures = Corelation.index
plt.figure(figsize = (9 , 5))
g = sns.heatmap(CVD[top_fatures].corr() , annot = True , cmap ='CMRmap_r')